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Stellenbosch University (2020)

AN INTEGRATED APPROACH TO GRASSLAND PRODUCTIVITY MODELLING USING SPECTRAL MIXTURE ANALYSIS, PRIMARY PRODUCTION AND GOOGLE EARTH ENGINE

Vermeulen, Liezl Mari

Titre : AN INTEGRATED APPROACH TO GRASSLAND PRODUCTIVITY MODELLING USING SPECTRAL MIXTURE ANALYSIS, PRIMARY PRODUCTION AND GOOGLE EARTH ENGINE

Auteur : Vermeulen, Liezl Mari

Université de soutenance : Stellenbosch University

Grade : Master of Arts (MA) 2020

Résumé partiel
Grassland degradation can have a severe impact on condition, productivity and consequently grazing potential. Current conventional methods for monitoring and managing grasslands are time-consuming, destructive and not applicable at large-scale. These constraints could be addressed using a remote sensing (RS)-based approach, however, current RS-based approaches also have technological and scientific limitations in the context of grassland management. The inability of RS-based primary production models to discriminate between herbaceous and woody production at sub-pixel level poses constraints for use in grazing capacity (GC) calculation. The integration of fractional vegetation cover (FVC) is posed as a promising solution, specifically estimation using spectral mixture analysis (SMA). Current grassland monitoring approaches are limited by the technological constraints of traditional, desktop-based RS approaches, but the implementation of analysis in a Google Earth Engine (GEE) web application can address these limitations by providing dynamic, continuous productivity estimates. Field data collection and analysis of biophysical parameters were performed to establish crucial relationships between vegetation productivity and RS signals. Biophysical parameters obtained include FVC, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and grass dry matter (DM) production. An important outcome was the improvement of the normalised difference vegetation index (NDVI) and fAPAR regression relationship, achieved by scaling fAPAR using the proportion of green, living biomass. The relationship proved useful in subsequent vegetation productivity modelling. The potential of SMA for FVC estimation using medium resolution imagery (Landsat 8 and Sentinel-2) and relatively few field points, was explored. A linear spectral mixture model (LSMM) was calibrated, implemented and evaluated on accuracy and transferability. A number of bands and spectral indices were identified as core features, specifically the dry bare-soil index (DBSI). DBSI improved discrimination between bare ground and dry vegetation, a common challenge in semi-arid conditions. The calibrated LSMM performed well, with Sentinel-2 providing the most accurate results.

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